Methods of diagnosing and predicting renal disease

ABSTRACT

This disclosure relates to methods of diagnosing and predicting renal disease, using one, two, or more biomarkers, including sTN-FR1, sTNFR2, sFAS, TNF, and IL-6.

CLAIM OF PRIORITY

This application claims priority under 35 USC §119(e) to U.S.Provisional Patent Application Ser. No. 61/177,074, filed on May 11,2009, and U.S. Provisional Patent Application Ser. No. 61/121,398, filedon Dec. 10, 2008, the entire contents of which are hereby incorporatedby reference.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Grant No. DK-41526awarded by the National Institutes of Health. The Government has certainrights in the invention.

TECHNICAL FIELD

This disclosure relates to methods of diagnosing and predicting earlyrenal function decline (ERFD), using biomarkers sTNFR1, sTNFR2, sFAS,TNF, and IL-6.

BACKGROUND

The traditional model of the development of end-stage renal disease(ESRD) in type 1 diabetes (T1DM), in which microalbuminuria (MA) leadsto proteinuria and then proteinuria is followed by renal function loss,has been challenged recently. Increase in urinary albumin excretion isan important determinant of diabetic nephropathy progression, but itdoes not entirely explain this phenomenon. For example, the loss ofrenal function commences earlier than previously recognized and precedesthe onset of proteinuria (Perkins et al., J Am Soc Nephrol.18:1353-1361, 2007). A longitudinal study of T1DM (the 1st Joslin Studyof Natural History of Microalbuminuria) showed that renal functiondecline began with the onset of MA in about one third of patients andprogressed in a linear fashion from normal kidney function to renalinsufficiency (Perkins et al., 2007, supra). In addition, renal functiondecline occurred in a noticeable proportion of patients with T1DM andnormal albumin excretion (Perkins et al., 2007, supra; Caramori et al.,Diabetes 52:1036-1040, 2003).

SUMMARY

As shown herein, progressive early renal function decline (ERFD), e.g.,in type 1 diabetes (T1DM) and type 2 diabetes begins while glomerularfiltration rate (GFR) is in the normal or elevated range and beforeonset of proteinuria. Inflammation and apoptosis may be involved in thisprocess. The present methods can be used to identify diabetic subjectswith early renal function decline, based on serum markers of the TNFpathway (e.g., TNFα, sTNFR1, and sTNFR2), the Fas pathways (e.g., sFas),and IL-6. The present methods can also be used to identify or predictdiabetic subjects at risk for developing end stage renal disease (ESRD),based on serum or plasma markers of the TNF pathway (e.g., sTNFR1).

In some embodiments, the present disclosure provides methods fordetermining (e.g., predicting or diagnosing) whether a human subject hasan increased risk of developing early renal function decline (ERFD).These methods can include obtaining a sample from a human subject whohas normoalbuminuria (NA), microalbuminuria (MA), or proteinuria (PT),and measuring the levels of one or more (including all) biomarkersselected from the group consisting of TNFa, soluble TNF receptor type 1(sTNFR1), soluble TNFR2 (sTNFR2), soluble Fas (sFas), and interleukin-6(IL-6), in the subject sample. These measured levels can then becompared with suitable reference levels of the one or more biomarkers.In some aspects, this comparison, or observations obtained from such acomparison, can be used to determine whether the subject has anincreased risk of developing ERFD. For example, a difference between thelevels of the one or more biomarkers in the subject sample and thereference levels can indicate that the subject has an increased risk ofdeveloping ERFD. In some cases, the difference can be that the levels ofthe one or more biomarkers in the subject sample are higher (e.g.,significantly higher) than the reference levels of the same biomarkers.In some instances, the subject sample can include serum from thesubject. In some cases, the subject can be a subject with diabetes,e.g., Type 1 or Type 2 diabetes. For example, the subject can beselected because they have diabetes, e.g., Type 1 or 2 diabetes.Furthermore, the subject can be a subject with normoalbuminuria,microalbuminuria, or proteinuria. For example, the subject can beselected because they have normoalbuminuria, microalbuminuria, orproteinuria. Methods for identifying subjects with normoalbuminuria,microalbuminuria, or proteinuria are known in the art and are disclosedbelow. In some instances, biomarkers measured in subjects with Type 1diabetes can include, e.g., sTNFR1, sTNFR2, and sFas; TNFa, sTNFR1,sTNFR2, and sFas; or TNFa, sTNFR1, sTNFR2, sFas, and IL-6. In someinstances, biomarkers measured in subjects with Type 2 diabetes caninclude, e.g., TNFa, sTNFR1, sTNFR2, sFas, and IL-6. In some instances,the human subject can be a subject that does not present any clinicalsigns or symptoms of chronic heart disease (CHD) or ischemic heartdisease. Furthermore, in some cases, the human subject can be a subjectthat has a glomerular filtration rate (GFR) of 90 mL/minute or more.

In some embodiments, the present disclosure provides methods fordetermining whether a human subject has an increased risk of developingchronic kidney disease (CKD), or end stage renal disease (ESRD), orboth. Such methods can include obtaining a sample from a human subjectwho has proteinuria, and measuring the level of soluble TNF receptortype 1 (sTNFR1) in the subject sample. These measured levels can then becompared with suitable reference levels (e.g., a reference levels ofsTNFR1). In some aspects, this comparison, or observations obtained fromsuch a comparison, can be used to determine whether the subject has anincreased risk of developing CKD, ESRD, or both. For example, adifference between the levels of the sTNFR1 in the subject sample andthe reference levels can indicate that the subject has an increased riskof developing CKD, ESRD, or both. In some cases, the difference can bethat the levels of the sTNFR1 in the subject sample are higher (e.g.,significantly higher) than the reference levels. In some instances, thehuman subject can be a subject with Type 1 or Type 2 diabetes. In someinstances, the human subject can be a subject with Type 1 diabetesand/or proteinuria.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Methods and materials aredescribed herein for use in the present disclosure; other, suitablemethods and materials known in the art can also be used. The materials,methods, and examples are illustrative only and not intended to belimiting. All publications, patent applications, patents, sequences,database entries, and other references mentioned herein are incorporatedby reference in their entirety. In case of conflict, the presentspecification, including definitions, will control.

Other features and advantages of the invention will be apparent from thefollowing detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

FIGS. 1A-1F are 3-D bar graphs showing mean cC-GFR in the studypopulation of individuals with type 1 diabetes according to albuminuriastatus (NA=normoalbuminuria and MA=microalbuminuria) and tertile (T1,T2, T3) of an inflammatory marker: 1A, sTNFR1; 1B, sTNFR2; 1C, TNFα; 1D,sFas; 1E, sICAM-1; 1F, IP10). P value for trend across the tertiles inNA (light grey bars) and in MA (dark grey bars), respectively.

FIGS. 2A-2B are bar graphs showing the proportion of the event=renalfunction loss defined as the top quartile of the fastest progression inthe prospective 2nd Joslin Kidney Study (2A) and in the replicative 1stJoslin Kidney Study (2B), stratified by tertiles of the respectivemarker.

FIGS. 3A-3D are 3-D bar graphs showing adjusted mean GFR (ml/min/1.73m²) in subjects with Type-2 Diabetes according to Albumin excretion ratestatus (NA=normoalbuminuria, MA=microalbuminuria, and PT=proteinuria)and tertile (T1, T2, T3): 3A, TNFα; 3B, IL6; 3C, sTNFR1; 3D, sFas.

FIG. 4 is a bar graph showing the association between TNFα, sTNFR1,sTNFR2, IL-6, and sFas in subjects with type II diabetes. Data shown thedifference in mean GFR (ml/min/1.73 m²) between the highest, and thelowest tertiles of five markers (TNFα, sTNFR1, sTNFR2, IL6, and sFas) inthe crude analysis after adjustment for age, gender, and AER status.

DETAILED DESCRIPTION

Low-grade chronic inflammation is thought to be involved in thepathogenesis of diabetic nephropathy (Navarro et al., Cytokine. GrowthFactor. Rev. 17:441-450, 2006, and Galkina et al., J. Am. Soc. Nephrol.,17:368-377, 2006). Tumor necrosis factor alpha (TNFα/TNF) is a keymediator of inflammation and plays a role in apoptosis. In animalmodels, its effects on kidneys include reduced glomerular filtrationrate (GFR) and increased albumin permeability (Navarro, supra). TNFαmediates its signal via two distinct receptors, tumor necrosis factorreceptor 1 (TNFR1) and tumor necrosis factor receptor 2 (TNFR2), whichare membrane-bound and also present in soluble form in serum (Macewan etal., Cell. Signal., 14:477-492, 2002). TNFα mediates its inflammatoryeffects by induction of a broad spectrum of chemokines includinginterleukin 8 (IL8); monocyte chemotactic protein-1 (MCP1); interferongamma inducible protein 10 (IP-10) and adhesion molecules such asintercellular adhesion molecule-1 (ICAM-1) and vascular adhesionmolecule-1 (VCAM-1) (Segerer et al., J. Am. Soc. Nephrol., 11:152-176,2000; Wong et al., Clin. Exp. Immunol., 149:123-131, 2007).

The Fas pathway mediates apoptosis and may play a role in theprogression of diabetic nephropathy (Kumar et al., Nephron. Exp.Nephrol., 96:e77-e88, 2004; Kumar et al., Mol. Cell. Biochem.,259:67-70, 2004; Schelling et al., Lab. Invest., 78:813-824, 1998; andPerianayagam et al., J. Lab. Clin. Med., 136:320-327, 2000). The bindingof Fas ligand (FasL) to Fas, its membrane-bound receptor which is alsopresent in serum in soluble form (sFasL, sFas), leads to an apoptoticresponse (Baba et al., Nephrology, 9:94-99, 2004 and Ortiz et al.,Nephrol. Dial. Transplant., 14:1831-1834, 1999).

Interleukin-6 (IL-6) is a pleiotropic, proinflammatory cytokine that hasbeen associated with complications in diabetes. Specifically,cross-sectional studies of subjects with type 2 diabetes demonstratethat elevated serum levels of Il-6 are associated with diabeticnephropathy (Shikano et al., Nephron., 85:81-5, 2000). However, asimilar association between 11-6 and diabetic nephropathy has not beenreported for type 1 diabetes (Schram et al., Diabetologia., 48:370-8,2005; Niewczas et al., Clin J Am Soc Nephrol., 4:62-70, 2009).

The majority of studies on serum markers of TNFα-mediated inflammationand apoptosis in diabetic nephropathy have explored their associationwith MA and proteinuria rather than with GFR (Zoppini et al., J. Clin.Endocrinol. Metab., 86:3805-3808, 2001).

The large cross-sectional study described herein investigated whetherserum concentrations of markers including the TNFα and TNF-relatedmarkers (sTNFR1, sTNFR2, sICAM-1, sVCAM-1, IL8, MCP1, IP10), involved inFas-related apoptosis (sFasL and sFas), IL-6, and CRP, are associated,independently from albuminuria, with variation in renal function inpatients with T1DM who do not have proteinuria or advanced renalfunction impairment. This knowledge will facilitate the development ofnew diagnostic tools for identifying patients with early renal functiondecline and help the search for intervention protocols for high-riskpatients that may be more effective if implemented 5-10 years earlier inthe disease course.

The studies described herein also investigated whether serumconcentrations of TNFR1 are associated with GFR in subjects with variousstages of chronic kidney disease (CKD) and whether such an associationcan be used as a predictive marker of ESRD in such subjects.

In these studies, glomerular filtration rate was estimated by a cystatinC-based formula (cC-GFR) that was previously shown to be an accurate wayof evaluating renal function in patients with diabetes (Macisaac et al.,Diabet. Med., 24:443-448, 2007 and Perkins et al, J. Am. Soc. Nephrol.,16:1404-1412, 2005).

In some embodiments, the present disclosure provides methods ofdetermining whether a subject is predisposed to develop early renalfunction decline (ERFD). These methods can include generating a subjectprofile by obtaining a biological sample, e.g., a urine or blood (e.g.,serum and/or plasma) sample, from the subject, measuring the level of atleast one biomarker described herein in the sample, and comparing thelevel of the biomarker in the urine or blood sample with a predeterminedreference profile. A reference profile can include a profile generatedfrom one or more subjects who are known to be predisposed to developERFD (e.g., subjects in a study who later develop ERFD), and/or aprofile generated from one or more subjects who are not predisposed todevelop ERFD. A “predisposition to develop ERFD” is a significantlyincreased risk of developing ERFD, e.g., the subject is statisticallymore likely to develop ERFD than a “normal” subject (e.g., a subject whohas diabetes but does not have an increased risk of developing ERFD). Insome aspects, a subject with a predisposition to develop ERFD is onewhose sample contains one or more of the biomarkers disclosed herein inamounts that differ (e.g., significantly differ) from, or are above,below, greater than or equal to, less than or equal to, or about thesame as, depending on whether the reference represents a normal risksubject or a high risk subject, from the level of the same one or morebiomarkers in a reference profile. In some cases, the difference in thelevels of the one or more biomarkers can be e.g., about a factor of twoor at least about a factor of two (e.g., at least twice or half thelevel of the biomarker present in the reference profile), wherein thereference profile represents a subject who is not predisposed to developERFD.

In some embodiments, the subject can have one or more risk factors fordeveloping ERFD, e.g., duration of diabetes, elevated hemoglobin A1c(HbA1c) levels (e.g., above 8.1% or above 9%), age over 35 years,elevated plasma cholesterol levels, high mean blood pressure, elevatedalbumin to creatinine ratio (e.g., above about 0.6), and hyperglycemia(e.g., blood glucose of over about 200 mg/dL). In some embodiments, thesubject can have microalbuminuria (e.g., excretes 30-300 μg/minalbumin). In another aspect, the subject may not have microalbuminuriaand/or is a subject with normoalbuminuria (e.g., excretes about lessthan 30 μg/min) and/or has normal renal function (e.g., has serumcreatinine levels at less than 1.2 mg/dl). In some embodiments, thesubject can have type 1 or type 2 diabetes. Alternatively or inaddition, the subject can be non-diabetic. In some embodiments, thesubject can have proteinuria, e.g., macroalbuminaria (e.g., the subjectexcretes more than about 300 μg/min albumin). In some embodiments, thesubject does not have, does not have a diagnosis of, or does not presentany clinical signs or symptoms of, chronic heart disease (CHD). In someembodiments, the subject does not have, does not have a diagnosis of, ordoes not present any clinical signs or symptoms of, ischemic heartdisease.

In some embodiments, the present disclosure provides methods ofdetermining whether a subject is predisposed to develop end stage renaldisease (ESRD). These methods can include generating a subject profileby obtaining a biological sample (e.g., a urine or blood (e.g., serum)sample) from the subject, measuring the level of at least one biomarkerdescribed herein in the sample, and comparing the level of the biomarkerin the urine or blood sample with a predetermined reference profile. Insome embodiments, these methods include generating a subject profile byobtaining a biological sample (e.g., a urine or blood (e.g., serum)sample) from the subject, measuring the level of TNFR1 in the sample,and comparing the level of TNFR1 sample with a predetermined TNFR1reference profile. Reference profiles can include a profile generatedfrom one or more subjects who are known to be predisposed to developESRD (e.g., subjects in a study who later develop ESRD), and/or profilesgenerated from one or more subjects who are not predisposed to developESRD. A “predisposition to develop ESRD” is a significantly increasedrisk of developing ESRD, i.e., the subject is more likely to developESRD than a “normal” subject, i.e., a subject who has diabetes but doesnot have an increased risk of developing ESRD. In some embodiments, asubject with a predisposition to develop ESRD is one whose sample has alisted biomarker (e.g., TNFR1) in amounts that significantly differfrom, or are above, below, greater than or equal to, less than or equalto, or about the same as the level of the same biomarker in thereference profile, depending on whether the reference represents anormal risk subject or a high risk subject. In some cases, thedifference in the levels of the one or more biomarkers can be, e.g.,about a factor of two or at least about a factor of two (e.g., at leasttwice or half the level of the biomarker present in the referenceprofile), wherein the reference profile represents a subject who is notpredisposed to develop ESRD.

In some embodiments, the subject can have one or more risk factors fordeveloping ESRD. Such factors can include, but are not limited to, e.g.,duration of diabetes, elevated hemoglobin A1c (HbA1c) levels (e.g.,above 8.1% or above 9%), age over 35 years, elevated plasma cholesterollevels, high mean blood pressure, elevated albumin to creatinine ratio(e.g., >0.6), and hyperglycemia (e.g., blood glucose of over 200 mg/dL).In some embodiments, the subject can have normal kidney function (e.g.,GFR=90 mL/min or more). In some embodiments, the subject can havechronic kidney disease (CKD) (e.g., stage 1 CKD (e.g., GFR=90 mL/minuteor more)), stage 2 CKD (e.g., GFR=60 to 89 mL/minute), stage 3 CKD(e.g., GFR=30 to 59 mL/minute), stage 4 CKD (e.g., GFR=15 to 29 mL/min),or stage 5 CKD (e.g., GFR=less than 15 mL/min or on dialysis). In someembodiments, the subject has proteinuria (e.g., excretion greater thanor equal to 300 μg/min albumin). In some embodiments, the subject hasCKD (e.g., stage 1, 2, 3, 4, or 5 CKD) and proteinuria. In someembodiments, the subject has diabetes (e.g., type 1 or type 2 diabetes).In some embodiments, the subject is a non-diabetic.

In some embodiments, the methods can include measuring the level of aplurality of the biomarkers described herein, e.g., one or morebiomarkers (e.g., 2, 3, 4, 5, or all of the biomarkers) can be measured.The level(s) of the biomarker(s) can be used to generate a biomarkerprofile for the subject.

The methods described herein can also include obtaining a sample from asubject, e.g., a blood or urine sample, and determining the level of thebiomarker(s) in the sample.

In some embodiments, the methods include normalizing for urinecreatinine concentrations.

The methods described herein can include contacting a sample obtainedfrom a subject with biomarker-specific biomolecules, e.g., an array ofimmobilized biomarker-specific biomolecules, and detecting stable ortransient binding of the biomolecule to the biomarker, which isindicative of the presence and/or level of a biomarker in the sample.The subject biomarker levels can be compared to reference biomarkerlevels obtained from reference subjects. Reference biomarker levels canfurther be used to generate a reference profile from one or morereference subjects. In one aspect, the biomarker-specific biomoleculesare antibodies, such as monoclonal antibodies. In another aspect, thebiomarker-specific biomolecules are antigens, such as viral antigensthat specifically recognize the biomarkers. In yet another aspect, thebiomarker-specific biomolecules are receptors (e.g., the TNF receptor).

The disclosure also features a pre-packaged diagnostic kit for detectinga predisposition to ERFD. The kit can include biomarker-specificbiomolecules as described herein and instructions for using the kit totest a sample to detect a predisposition to ERFD. The kit can also beused to determine the efficacy of a therapy administered to prevent ERFDby contacting the biomarker-specific biomolecules with a sample obtainedfrom a subject undergoing a selected therapy. The level of one or morebiomarkers in the sample can be determined and compared to the level ofthe same one or more biomarkers detected in a sample obtained from thesubject prior to, or subsequent to, the administration of the therapy.Subsequently, a caregiver can be provided with the comparisoninformation for further assessment.

Biomarkers

In some embodiments, the methods described herein include themeasurement of levels of certain soluble biomarkers, including one ormore of sTNFR1, sTNFR2, sFAS, TNF, and IL-6. Specific alterations in oneor more of the biomarkers listed herein are statistically related to thedevelopment of ERFD. These biomarkers serve as early biomarkers fordisease, and characterize subjects as at high risk for future disease.The systematic names of the molecules are as follows:

TNFa: Tumor Necrosis Factor; TNF (TNF superfamily, member 2); EntrezGenelD: 7124; mRNA: NM_000594.2; protein: NP_000585.2.

soluble TNF Receptor type 1 (sTNFR1): soluble Tumor Necrosis FactorReceptor Subfamily, member 1A; sTNFRSF1A; Entrez GeneID: 7132; mRNA:NM_001065.2; protein: NP_001056.1; see also WO9531544; Fernandez-Botranet al., FASEB J. 5(11):2567, 1991; and US2006039857.

soluble TNF receptor type 2 (sTNFR2): soluble Tumor Necrosis FactorReceptor Subfamily, member 1B; sTNFRSF1B; Entrez GenelD: 7133; mRNA:NM_001066.2; protein: NP_001057.1; see also WO9531544; Fernandez-Botranet al., FASEB J. 5(11):2567, 1991; and US2006039857.

soluble Fas (sFas): soluble Tumor Necrosis Factor Receptor Superfamily,member 6 (sTNFRSF6); Entrez GenelD: 355; mRNA: NM_000043.3, NM_152871.1,NM_152872.1, NM_152873.1, NM_152874.1, NM_152875.1, NM_152876.1, orNM_152877.1; Protein: NP_000034.1, NP_690610.1, NP_690611.1,NP_690612.1, NP_690613.1, NP_690614.1, NP_690615.1, or NP_690616.1. Seealso U.S. Pat. No. 5,652,210; Chen et al., Science, 263:1759-1762, 1994;Hachiya et al., and WO 96/01277.

Interleukin-6 (IL6); Entrez GeneID; 3569; mRNA: NM_000600.3; Protein:NP_000591.1.

In some embodiments, other markers, e.g., urinary or serum biomarkers,of renal failure can also be used, as are known in the art.

A “subject” level can also be referred to as a “test” profile. A subjectlevel can be generated from a sample taken from a subject prior to thedevelopment of microalbuminuria (e.g., when the subject is excretingless than 30 mg of albumin a day or has an albumin-creatinine (A/C)ratio of less than 30 in a random urine specimen). Thus, a “subject”level is generated from a subject being tested for predisposition to DN.

A “reference” level can also be referred to as a “control” level. Areference level can be generated from a sample taken from a normalindividual or from an individual known to have a predisposition to ERFD,or from an individual known to have ESRD and/or CKD. The referencelevel, or plurality of reference levels, can be used to establishthreshold values for the levels of, for example, specific biomarkers ina sample. A “reference” level includes levels generated from one or moresubjects having a predisposition to ERFD, levels generated from one ormore subjects having ESRD and/or CKD, or levels generated from one ormore normal subjects.

A reference level can be in the form of a threshold value or series ofthreshold values. For example, a single threshold value can bedetermined by averaging the values of a series of levels of a singlebiomarker from subjects having no predisposition to ERFD. Similarly, asingle threshold value can be determined by averaging the values of aseries of levels of a single biomarker from subjects having apredisposition to ERFD. Thus, a threshold value can have a single valueor a plurality of values, each value representing a level of a specificbiomarker, detected in a urine sample, e.g., of an individual, ormultiple individuals, having a predisposition to ERFD.

As described herein, a subject level can be used to identify a subjectat risk for developing ERFD based upon a comparison with the appropriatereference level or levels. Subjects predisposed to having ERFD can beidentified prior to the development of microalbuminuria or withmicroalbuminuria by a method described herein. For example, a subjectlevel of a biomarker described herein detected in a sample from asubject can be compared to a “reference” level of the same biomarkerdetected in a sample obtained from a reference subject. If the referencelevel is derived from a sample (or samples) obtained from a referencesubject having a predisposition to ERFD, then the similarity of thesubject level to the reference level is indicative of a predispositionto ERFD for the tested subject. Alternatively, if the reference level isderived from a sample (or samples) obtained from a reference subject whodoes not have a predisposition to ERFD, then the similarity of thesubject level to the reference level is not indicative of apredisposition to ERFD for the tested subject. As used herein a subjectlevel is “similar” to a reference level if there is no statisticallysignificant difference between the two levels. In some embodiments, asubject level differs significantly from the reference level of the samebiomarker(s), when the reference level is from a reference subject whodoes not have a predisposition to ERFD, is indicative of apredisposition to ERFD in the subject.

In some embodiments, the biomarker for ERFD can include one or more of,e.g., TNFα, sTNFR1, sTNFR2, Fas, and IL-6, including any combination ofTNFα, sTNFR1, sTNFR2, Fas, and IL-6. In some embodiments, the biomarkerfor ERFD can include TNFα, sTNFR1, sTNFR2, Fas, and IL-6.

In some embodiments, the biomarker for end stage renal disease (ESRD)can include sTNFR1.

Methods of Detection

Any method known in the art for determining levels of an analyte in abiological sample can be used. An exemplary biochemical test foridentifying levels of biomarkers employs a standardized test format,such as the Enzyme Linked Immunosorbent Assay (ELISA) (see, e.g.,Molecular Immunology: A Textbook, edited by Atassi et al.; Marcel DekkerInc., New York and Basel 1984, for a description of ELISA tests). Insome embodiments, the biochemical test can include a multiplexparticle-enhanced immunoassay with a flow cytometry based detectionsystem (e.g., LUMINEX®).

It is understood that commercial assay kits (e.g., ELISA) for variouscytokines and growth factors are available. For example, ELISA kits areavailable from R&D systems. sFas can be measured, e.g., using theQuantikine Human sFas Immunoassay from R&D Systems. Arrays and chipsknown in the art can also be used.

EXAMPLES

The invention is further described in the following examples, which donot limit the scope of the invention described in the claims.

Example 1: Concentrations of Markers of TNFα and Fas-Mediated Pathwaysand Renal Function in Non-Proteinuric Patients with Type 1 Diabetes

Characteristics of the Study Population

The study group was selected from the population attending the JoslinClinic, a major center for the treatment of patients of all ages withT1DM or T2DM. The population is about 90% Caucasian, and most reside ineastern Massachusetts. Detailed descriptions of the Joslin Clinicpopulation and the recruitment protocol for this study have beenpublished previously (Rosolowsky et al., Clin. J. Am. Soc. Nephrol.,2008). Eligibility criteria included residence in New England, diabetesdiagnosed before age 40 years, treatment with insulin, current age 18-64years, diabetes duration 3-39 years, and multiple measurements in thepreceding two-year interval of hemoglobin A1c (HbA1c) and urinaryalbumin-to-creatinine ratio (ACR). For each patient, the measurements ofHbA1c were summarized by the mean, and the measurements of ACR by themedian. Exclusion criteria included proteinuria (median ACR ≧250 for menand ≧355 μg/min for women), ESRD, other serious illness, extreme obesity(body mass index >40 kg/m²), or a median HbA1c less than 6.5% (nearnormoglycemia).

Trained recruiters administered a structured interview and briefexamination to eligible patients at a routine visit to the clinic, theenrollment visit. The interview solicited the history of diabetes andits treatment, other health problems, and use of medications. Therecruiter measured seated blood pressure twice (five minutes apart) withan automatic monitor (Omron Healthcare, Inc) and averaged them to reducevariability and also obtained samples of blood and urine.

Current and past use of medications (particularly ACE inhibitors,Angiotensin II Receptor Blockers, and other antihypertensive drugs) wasrecorded during the enrollment interview and supplemented by examinationof clinic records to confirm prescription dates. All archived clinicallaboratory measurements of HbA1c, ACR and serum cholesterol were alsoextracted. Details of the assays used were described previously(Ficociello et al., Clin J. Am. Soc. Nephrol., 2:461-469, 2007, andKrolewski et al., N. Engl. J. Med., 332:1251-1255, 1995). ACR valueswere converted to Albumin Excretion Rate (AER) according to a formulapublished previously (Krolewski, supra). For characterizing patients'recent exposures, repeated measures were summarized by their median(AER) or mean (HbA1c, cholesterol, lipids).

Enrollment blood samples were drawn by venipuncture into sterilecollection tubes (SST Plus BD Vacutainer) (BD, NJ, USA), centrifuged at3600 rpm for 10 minutes at 6° C. (Centrifuge 5810 R, USA) and thenaliquoted into 1.5 ml sterile, non-toxic non-pyrogenic tubes cryogenictubes (CRYOTUBES™ CRYOLINE™ System) [NUNC™ Serving Life Science, USA]and frozen at −80° C. until further analysis. Length of storage, definedas the interval between the dates of sample collection andassay-determination (range 2 to 5 years), was included as a covariate inthe analysis to estimate the extent of degradation of each analyteduring storage.

Serum cystatin C concentration (Dade Behring Diagnostics) was assayed ona BN PROSPEC™ System nephelometer (Dade Behring Incorporated, Newark,Del., USA). The range of detection was 0.30 to 7.50 mg/L, and thereported reference range for young, healthy persons was 0.53 to 0.95mg/L. The intra-individual coefficient of variation for subjects withdiabetes was 3.8 and 3.0 percent in samples from the lowest and highestquartiles of the cystatin C distribution, respectively (Perkins et al.,J. Am. Soc. Nephrol., 18:1353-1361, 2007).

The estimated glomerular filtration rate (cC-GFR ml/min) is thereciprocal of cystatin C (mg/L) multiplied by 86.7 and reduced bysubtracting 4.2. This formula was recently developed by MacIsaac et al.,supra, as a reliable estimate of GFR in patients with diabetes. Themethod used for measuring cystatin-C was similar with respect to assay,equipment, and coefficient of variation as that reported by MacIsaac,supra.

The study group included 667 participants: 304 with MA and 363 withnormoalbuminuria. Selected characteristics at their enrollment aresummarized in Table 1 according to AER group. In the NA group, the 25th,50th and 75th percentiles of the AER distribution (11, 15, and 21μg/min) were centered in the NA range (<30 μg/min)], but in the MA groupthese AER percentiles (45, 69, 131 μg/min) were entirely in the lowerhalf of the MA range (30-300 μg/min). In comparison with the NA group,the MA group had an older age, higher proportion of men, longer durationof diabetes, higher HbA1c and significantly lower cC-GFR. The differencein cC-GFR between the two study groups was clearer when renal functionwas grouped into categories, the latter two of the four corresponding tomild and moderate renal function impairment, present in 36% of the MAgroup but only in 10% of the NA group.

TABLE 1 Characteristics of the study group according to albuminuriastatus. NORMO- MICRO- ALBU- ALBU- MINURIA MINURIA Characteristics (n =363) (n = 304) P AER* (μg/min) 15 (11-21) 69 (45-131) by design Age(yrs) 39 ± 12 41 ± 12 <0.05 Male (%) 44% 61% <0.0001 Diabetes duration(yrs) 20 ± 9  23 ± 10 <0.0001 HbA1c† (%) 8.3 ± 1.2 8.6 ± 1.5 <0.01cC-GFR‡ (ml/min/ 118 ± 24  99 ± 27 <0.0001 1.73 m²) cC-GFRcategories: >130 ml/min 30% 10% 90-130 61% 54% 60-89  9% 28% <60  1%  8%Data are mean ± standard deviation or median (quartiles) or %. *AER:median albumin excretion rate during the preceding 2-year window †HbA1c:mean hemoglobin A1c during the preceding 2-year window ‡cC-GFR:estimated glomerular filtration rate based on serum cystatin-C

To distinguish the relative contributions of AER and various clinicalcharacteristics to the large variation in renal function within thestudy group, the NA and MA groups were divided at the group-specificmedian cC-GFR (115 and 101 nil/min, respectively) into groups withhigher and lower cC-GFR (Table 2). The median (25th, 75th percentiles)of the resulting distributions of cC-GFR in the NA groups were 136 (125,148) and 102 (92, 109) ml/min and in the MA groups were 115 (108, 124)and 82 (64, 91) ml/min. All of the characteristics in Table 2 weresignificantly different between NA and MA groups, but many were notsignificantly different between the groups with higher and lower cC-GFR(two-way ANOVA). For example, the expected associations of higher HbA1c,systolic blood pressure and serum cholesterol with MA were present, aswere the associations of cigarette smoking and treatment with an ACEi orARB. However, none of these characteristics were associated with lowercC-GFR. In contrast, older age and longer diabetes duration weresignificantly associated with both MA and lower cC-GFR, as was evidenceof medical attention represented by treatment with antihypertensive orlipid lowering agents.

TABLE 2 Characteristics of the study group according to albuminuriastatus and group-specific median cC-GFR. NORMOALBUMINURIAMICROALBUMINURIA GROUP CONTRAST cC-GFR >115 cC-GFR <115 cC-GFR >101cC-GFR <101 AER cC-GFR Characteristic (n = 183) (n = 180) (n = 152) (n =152) P* P† AER 13 (10-18) 18 (12-23) 56 (42-100) 85 (51-161) By Design<0.0001 (μg/min) Age (y)  37 ± 11  40 ± 13  36 ± 12  45 ± 11 <0.05<0.0001‡ Diabetes 19 ± 9  21 ± 10 20 ± 9 26 ± 9 <0.0001 <0.0001‡duration (y) HbA1c (%)  8.3 ± 1.2  8.3 ± 1.2  8.7 ± 1.6  8.4 ± 1.4<0.005 ns BMI (kg/m²) 25.6 ± 3.6 26.7 ± 4.3 27.2 ± 4.8 27.7 ± 5.2<0.0005 <0.05 Systolic BP 118 ± 12 120 ± 13 124 ± 12 125 ± 15 <0.0001 ns(mmHg) ACEI or 18% 21% 49% 55% <0.0001 ns ARB Rx (%) Anti-  7% 16% 14%30% <0.001 <0.0001 hypertensive Rx (%) Serum 183 ± 29 181 ± 29 190 ± 33193 ± 30 <0.0001 ns Cholesterol (mg/dl) Lipid 24% 34% 31% 42% <0.05<0.005 lowering Rx (%) Current  9% 12% 19% 18% <0.005 ns smoker (%) Dataare mean ± standard deviation or median (quartiles) or %. *P-value forthe albuminuria main effect in an ANOVA †P-value for the cC-GFR maineffect in an ANOVA;

Serum Markers of Inflammation or Apoptosis and Impaired Renal Function

All markers were measured by immunoassay. Samples were thawed, vortexedand centrifuged, and measurements were performed in the supernatant.sTNFR1, sTNFR2 and IL-6 were measured using enzyme-linkedimmunoadsorbent assay (ELISA) (DRT100, DRT200 and high sensitiveimmunoassay HS600B, respectively) (R&D, Minneapolis, Minn., USA)according to the manufacturer's protocol. Interleukin-6 (IL-6) wasmeasured in only a subset of the study group (156 individuals). Theserum concentrations of the other protein markers were measured in amultiplex assay run on the Luminex platform. This is a multiplexparticle-enhanced, sandwich type, liquid-phase immunoassay withlaser-based detection system based on flow cytometry. Adipokine-panel B(HADK2-61K-B) [Linco-Milipore, USA] was used to measure TNFα; humanSepsis-Apoptosis Panel (HSEP-63K) [Linco-Milipore, USA] was used tomeasure sFas, sFasL, sICAM-1 and sVCAM-1; and Beadlyte® HumanMulti-Cytokine Detection (48-011) [Upstate-Milipore, USA] with protocolB was used to measure IL8, IP10, MCP1. Protocols provided by vendorswere followed. Briefly, the method included use of 96-well filter plates[Milipore, USA], the capture antibodies specific for each analyte boundcovalently to fluorescently labeled microspheres, biotinylated detectionantibodies and streptavidin-phycoerythrin. Detection incorporates twolasers and a high-tech fluidics system [Luminex 100S, Austin, Tx, USA].Values of median fluorescence intensity were fitted to a 5-parameterlogistic standard curve (Gottschalk et al., Anal. Biochem., 343:54-65,2005).

Assay sensitivities were: TNFα, 0.14 pg/ml; sTNFR1 and sTNFR2, 0.77pg/ml; sFas, 7 pg/ml; sFasL, 6 pg/ml; sICAM-1, 30 pg/ml; sVCAM-1, 33pg/ml; IL8, 0.7 pg/ml; IP10, 1.2 pg/ml; MCP1, 1.9 pg/ml; IL-6, 0.04pg/ml. If required, samples were diluted (sTNFR1, sTNFR2, sFAS, sFASL,sICAM-1, and sVCAM-1). The number of freeze-thaw cycles was one for allmeasurements of TNFα, IL8, IP10, MCP1 and for the majority ofmeasurements of the other analytes. The number did not exceed two forany measurement.

Two internal serum controls were prepared in the same manner as studysamples and were stored in a large number of aliquots at −80° C.Aliquots of the two controls were included in each assay (Aziz et al.,Clin. Diagn. Lab. Immunol., 5:755-761, 1998) for estimating theinter-assay CV. For most assays, inter-assay CV was between 8.5% and15.8% (15.8% TNFα, 13.0% sTNFR1, 12.7% sTNFR2, 8.5% sFas, 13.5% sFasL,8.1% sVCAM-1, and 14.7% IP10). It was higher for the remaining three(25.2% sICAM-1, 33.3% IL8, and 28.4% MCP1). Immunoassay for TNFα, sFasand sFasL detects the free form of the protein, whereas ELISA for sTNFR1and sTNFR2 detects the total amount of protein, free and bound withtheir ligand TNFα, (information provided by manufacturer).

Serum concentrations of markers of inflammation or apoptosis wereexamined in the same manner as the characteristics shown in Table 2.Four markers (sTNFR1, sTNRF2, sFas, and sICAM-1) were significantlyassociated both with AER and with cC-GFR (Table 3). TNFα and IP-10) weresignificantly associated only with cC-GFR group and two (IL-8 and CRP)were significantly associated only with AER group.

Analyses were done in SAS (SAS Institute, Cary, N.C., version 9.1.3).T-tests and Chi-square tests with alpha=0.05 were used for continuousvariables and frequencies, respectively. Analyses in Tables 2 and 3 andFIG. 1 were ANOVAs for unbalanced design. Linear regression with cC-GFRas dependent variable was used for multivariate analysis. AER and serumconcentrations of the markers were transformed to their logarithms foranalysis. Missing data for serum markers never decreased the studysample by more than 5% in any model, so no remedial action was taken.

For the six markers significantly associated with cC-GFR in Table 3, thepatterns of association are illustrated in FIGS. 1A-F. Separately forthe NA and MA groups, patients were grouped according to the tertiles ofthe distribution of each marker, and the mean cC-GFR for each subgroupwas depicted as a vertical bar. In both AER groups, the decrease incC-GFR with increasing marker concentration was steepest for sTNFR1 andsTNFR2. The pattern was similar for TNFα but the differences amongsubgroups were smaller. For all three markers, the decrease appearssteeper in the MA group than in the NA group. For the remaining threemarkers (sICAM-1, IP10 and sFas), a pattern of differences amongsubgroups was less apparent.

TABLE 3 Serum concentrations of markers of inflammation or apoptosisaccording to AER group and cC-GFR above or below median NORMOALBUMINURIAMICROALBUMINURIA GROUP CONTRAST cC-GFR >115 cC-GFR <115 cC-GFR >101cC-GFR <101 AER cC-GFR (n = 182) (n = 181) (n = 152) (n = 152) P* P†TNF-mediated pathway TNFα pg/ml 3.6 (2.3, 4.8) 3.9 (2.8, 5.8) 4.0 (2.6,5.4) 4.8 (3.3, 6.4) ns <0.005  sTNFR1 ng/ml 1.2 (1.0, 1.4) 1.4 (1.2,1.7) 1.4 (1.2, 1.6) 2.0 (1.6, 2.5) <0.0001 <0.0001 sTNFR2 ng/ml 2.1(1.7,2.6) 2.6 (2.1, 3.6) 2.3 (1.9, 2.9) 3.2 (2.5, 5.4) <0.0001 <0.0001Potential downstream effectors: Chemokines IL-8 pg/ml 4.4 (2.4, 10.4)6.1 (3.4, 13.3) 7.6 (3.8, 18.3) 7.0 (4.0, 15.5) <0.05  ns IP-10 pg/ml107 (79, 136) 122 (88, 171) 102 (75, 141) 115 (80, 158) ns <0.001  MCP-1pg/ml 124 (75, 184) 120 (77, 184) 113 (78, 191) 105 (77, 174) ns nsAdhesion molecules sICAM-1 ng/ml 133 (109, 152) 137 (119, 169) 149 (123,173) 152 (123, 191) <0.0005 <0.005  sVCAM-1 ng/ml 386 (301, 481) 389(303, 489) 376 (295, 467) 394 (330, 495) ns ns Fas-mediated pathwaysFasL pg/ml 0.12 (0.08, 0.19) 0.13 (0.07, 0.20) 0.12 (0.08, 0.18) 0.11(0.06, 0.16) ns ns sFas ng/ml 3.8 (3.0, 4.7) 4.5 (3.7, 5.5) 4.5 (3.6,5.6) 5.4 (3.7, 6.9) <0.0001 <0.0001 Other inflammatory markers CRP μg/ml1.2 (0.5, 3.2) 1.1 (0.6, 2.7) 1.4 (0.5, 3.9) 1.6 (0.8, 3.2) <0.05  nsIL-6 pg/ml 0.8 (0.6, 1.4) 0.9 (0.7, 1.5) 0.8 (0.4, 1.3) 0.9 (0.6, 2.2)ns ns Data are medians (quartiles); analyses were done on concentrationstransformed to their logarithms. *P-value for the albuminuria maineffect in an ANOVA; †P-value for the cC-GFR main effect in an ANOVA;

These markers were studied further by examining their correlations witheach other, and with the two nephropathy measures, cC-GFR and AER (Table4). The negative correlations between the six markers and cC-GFRrecapitulate the negative associations shown in Table 3 and FIG. 1. Allpairs of markers are significantly correlated, but the coefficients aregenerally modest. Only the correlation of the two receptors (sTNFR1 andsTNFR2) with cC-GFR and with each other exceeded 0.50. Note the poor(although significant) correlations between TNFα and its receptors(r=0.11 for TNFα/sTNFR1 and r=0.20 for TNFα/sTNFR2).

TABLE 4 Spearman correlation coefficients between cC-GFR, AER, and serummarkers of inflammation and apoptosis in the study group AER TNFa sTNFR1sTNFR2 sFas IP-10 sICAM cC-GFR −0.31 −0.15 −0.57 −0.56 −0.27 −0.13*−0.17 AER 1.00 0.11 0.41 0.28 0.04‡ −0.12† 0.20 TNFa 1.00 0.11* 0.200.34 0.19 0.17 sTNFR1 1.00 0.81 0.26 0.20 0.21 sTNFR2 1.00 0.32 0.260.27 sFas 1.00 0.14* 0.12* IP-10 1.00 0.14* sICAM 1.00 *p < 0.01, †p <0.05 ‡p = ns, otherwise all other p < 0.0001.

The independence of the associations of these six markers ofinflammation or apoptosis with cC-GFR was examined in multipleregression models. Only sTNFR1, sTNFR2 and sFAS remained significantwhen all were included in the model. Although sTNFR2 was statisticallysignificant in this model, its contribution was small due to its highcollinearity with sTNFR1, so it was not retained in subsequent modeling.Most notable about this model was that the serum markers alone (sTNFR1and Fas) explained 41% of the variation in cC-GFR (adjusted r2) andaddition of age and AER to the model increased the adjusted r2 to only45% (Table 5). Addition of the other clinical covariates from Table 2did not improve the adjusted r². The relative influence of thesecovariates on cC-GFR is summarized in Table 5 by the cC-GFR estimated atthe 25th, 50th and 75th percentiles of each covariate, with and withoutadjustment for other covariates.

TABLE 5 Mean cC-GFR at the 25^(th), 50^(th) and 75^(th) Percentiles ofEach Significant Covariate and the Corresponding Estimates Adjusted forthe Other Covariates Univariate analysis Multivariate analysis * cC-GFRcC-GFR Per- (ml/min/ p- (ml/min/ p- Covariate centile 1.73 m²) value1.73 m²) value Age [y] <0.0001 <0.002 31 25^(th) 115 114 40 50^(th) 109112 48 75^(th) 104 110 AER [μg/min] <0.0001 <0.000 22 25^(th) 119 115 3950^(th) 111 112 79 75^(th) 102 108 sTNFR1 [pg/ml] <0.0001 <0.000 121625^(th) 121 120 1442 50^(th) 112 112 1764 75^(th) 101 103 sFas [pg/ml]<0.0001 <0.008 3.63 25^(th) 112 113 4.50 50^(th) 110 112 5.72 75^(th)107 111 * Adjusted r² for the multivariate model was 0.45, whereas itwas 0.41 after adjustment for sTNFR1 and sFas only. Adjustments forgender, HbA1c, bmi, anti-hypertensive and lipid-lowering treatment, andduration of storage samples did not modify the associationssignificantly.

The effect on cC-GFR is the most pronounced for sTNFR1, and it is hardlychanged by multivariate adjustment. Adjustment for the other potentiallyrelevant clinical covariates—such as gender, hemoglobin A1c, body massindex, renoprotective and other antihypertensive treatment andlipid-lowering treatment, and duration of storage of serum specimens didnot modify the association of sTNFR1 and Fas with cC-GFR. When theanalysis was repeated using sTNFR2 instead of sTNFR1, the result wassimilar, indicating that measurement of either receptor yields roughlythe same information.

The primary focus of this study was on cC-GFR (not albuminuria) as anoutcome in early diabetic nephropathy and its attempt to differentiatethe observed effect of markers on GFR from their potential associationswith AER. Both uni- and multivariate analyses were performed. Inunivariate analyses, six markers were unrelated to renal function (CRP,IL-6, IL-8, MCP-1, sVCAM-1, and sFasL) and six were significantlyassociated with variation in cC-GFR (TNFα, sTNFR1, sTNFR2, sFas,sICAM-1, and IP10). Among the six, the associations of TNF receptorswith decreased cC-GFR were the strongest.

Based on multivariate analysis, of the six markers, only theconcentrations of sTNFR1, sTNFR2 and sFas contributed independently tocC-GFR. The effect of TNF receptors on cC-GFR was much more pronouncedthan the effects of clinical covariates as age and AER (Table 5).Furthermore, serum concentrations of sTNFR1 and sTNFR2 are highlycorrelated (Spearman r=0.81) and show roughly the same associations withcC-GFR.

This study provides evidence for the first time that markers of TNFα-and Fas-mediated pathways are strongly associated with variation incC-GFR in patients with T1DM and early diabetic nephropathy. Thisassociation is independent of the association of these markers with AER.These findings support the hypothesis that inflammation and apoptosisare involved in early renal function decline in T1DM.

Other cross-sectional studies in T1DM reported that serum concentrationsof TNFα-related markers were elevated in comparison with healthysubjects and that the higher concentrations of these markers wereassociated with elevated urinary albumin excretion (Zoppini, supra, andSchram et al., Diabetologia, 48:370-378, 2005). Cross-sectionalassociation between serum concentrations of sTNFRs and variation in GFRhas been shown in T2DM (Lin et al., Kidney. Int., 69:336-342, 2006) aswell as in non-diabetic individuals (Keller et al., Kidney. Int.,71:239-244, 2007 and Knight et al., J. Am. Soc. Nephrol., 15:1897-1903,2004). In the prospective CARE study, high serum concentrations ofsTNFR2 were found to be associated with faster progression of renalfunction loss (Tonelli et al., Kidney. Int., 68:237-245, 2005). However,all subjects in that study had chronic kidney disease (GFR<60ml/min/1.73 m²) at baseline.

One may argue that the association of TNFα receptors and cC-GFR simplyreflects impaired renal handling of these proteins. Indeed, thesereceptors are cleared mainly by the kidneys as shown by tracer studiesof radiolabelled sTNFR2 in animals (Bemelmans et al., Cytokine,6:608-615, 1994). Also serum concentrations of soluble TNF receptorsincrease in advanced renal failure, as demonstrated in bi-nephrectomizedmice (Bemelmans, supra) and in human studies (Brockhaus et al., Kidney.Int., 42:663-667, 1992). However, the majority of patients in this studyhad normal renal function, and even the renal function loss resultingfrom uni-nephrectomy does not raise serum sTNF receptor concentrationsin animals. Moreover, serum concentrations of sFasL, which has amolecular mass similar to soluble TNF receptors, is not associated withcC-GFR, while the receptors are strongly associated with variation incC-GFR. Based on those data potentially decreased clearance of thosemolecules is not the most likely explanation of these findings.

Adhesion molecules and chemokines are potential downstream effectors ofthe TNF-sTNFRs inflammatory pathway (Segerer, supra). Expression ofIL-8, MCP-1, and IP-10 mRNA is induced in TNFα-activated PBMNC takenfrom individuals with diabetes, but not from healthy ones (Wong, supra).Expression and serum concentrations of chemokines and adhesionmolecules, VCAM-1 and ICAM-1, increase as diabetic nephropathy develops(Wong, supra, and Nelson et al., Nephrol. Dial. Transplant.,20:2420-2426, 2005). In the univariate analysis described here, serumconcentrations of IP-10 and sICAM-1 were associated with variation incC-GFR and they correlated with their potential upstream regulators.Nevertheless, the observed effects were weak, and disappeared inmultivariate analysis, as one would expect if their effect were notindependent of the TNF receptors or sFas.

Analysis of the Fas-mediated pathway revealed an independent effect ofthe serum concentration of sFas on variation in cC-GFR and a lack of aneffect of the serum concentration of sFasL. A similar pattern ofdisparate effects of sFas and sFasL was previously demonstrated inindividuals with advanced kidney disease (Perianayagam, supra). Also, ina small number of individuals with T1DM and without proteinuria, sFaswas reported to correlate with both ACR and GFR (Protopsaltis et al.,Med. Princ. Pract., 16:222-225, 2007).

The mechanism of action of soluble Fas receptor has not been well knownbut may be similar to that of TNF receptors in that it leads to anenhanced Fas-mediated response in the kidney. The Fas-related system isinvolved mainly in regulation of apoptosis (Schelling, supra), whereasthe TNF-system regulates apoptotic and inflammatory responses.Consistent with this is the tubulointerstitial apoptosis seen instrepotozocin-induced diabetic rats (Kumar, supra) and in human diabetickidneys (Kumar, supra). Some evidence also suggests that TNFα may induceFas-mediated apoptosis (Elzey et al., J. Immunol. 167:3049-3056, 2001and Boldin et al., J. Biol. Chem., 270:387-391, 1995). In this studyserum concentrations of TNFα and sFas were markedly correlated.

The relatively poor correlations between TNFα and its receptors may haveresulted from low detection of TNFα bound to its receptors and itsassociation with cC-GFR being weaker than that of its receptors.

In conclusion, this study provides the first clinical evidence thatmarkers of the TNF- and Fas-mediated pathways are strongly associatedwith glomerular filtration rate in patients with T1DM and NA or MA.sTNFR1, sTNFR2 and sFas are the markers representing these associationsmost strongly.

Example 2: Serum Concentrations of TNFa, Soluble TNF Receptor Type 1 and2 and Fas Predict Strongly Early Renal Function Decline in HumanSubjects with Type 1 Diabetes and No Proteinuria and Carry StrongDiagnostic Potential

Glomerular filtration rate (GFR) starts to decline before proteinuriaoccurs in type 1 diabetes (Perkins et al., J Am Soc Nephrol.18:1353-1361, 2007). This phenomenon is referred to as “early renalfunction decline” (ERFD). The clinically approved diagnostic marker forprogression of diabetic nephropathy, microalbuminuria (MA), does notpredict renal function decline sufficiently at this early stage. First,the presence of MA is not necessary for renal function decline to occur.Second, only a proportion of people with MA develop renal functiondecline (Perkins et al., 2007, supra). There is an urgent need for noveldiagnostic tools that can identify patients at high risk of progressionand to implement enhanced therapeutic strategies.

In the population of 667 patients with type 1 diabetes and noproteinuria described in Example 1, serum concentrations of TNFa,soluble TNF receptor type 1 (STNFR1), soluble TNFR2, and soluble Faswere associated with lower GFR in the cross-sectional phase. The nextprospective phase of the study included the subset of 398 patients whowere followed for 3-5 years. Serum concentrations of TNFa, sTNFR1,sTNFR2 and Fas emerged as strong predictors of GFR decline with strengthat least comparable to microalbuminuria. Repeated measurements over timewere performed to evaluate intraindividual variation and their impact onthe prediction.

To validate these findings the study was replicated in an independentpopulation sample of type 1 diabetic population from the 1st JoslinKidney Study (n=299, observation period 8-12 years). The results, shownin FIGS. 2A-2B, demonstrated that the predictive effect of those fourmarkers is comparably strong.

In summary, the association of serum concentrations of TNFa, STNFR1,STNFR2, and sFas with ERFD were validated in subjects with type 1diabetes in the cross-sectional study (see Example 1), a prospectivestudy that included repeated measurements and consideration of theconfounding clinical factors, and were further replicated in theindependent population sample. This strongly demonstrates thatimplementation of those markers will significantly strengthen diagnosticalgorithm to identify subjects with type 1 diabetes and early diabeticnephropathy at high risk of renal function decline.

The former studies on inflammatory markers focused on albumin excretion,or on much more advanced stages of diabetic nephropathy. The presentresults demonstrate the strong diagnostic potential of those markers forGFR prediction, rather than albuminuria; as discussed above, GFR is amuch more accurate marker of ERFD than is albuminaria. Furthermore,these results (i.e., the prospective and replication studies)demonstrate the usefulness of these markers in the very early stages ofdiabetic nephropathy.

Example 3: Serum Concentrations of TNFα, Soluble TNF Receptor Types 1and 2, Soluble Fas, and IL-6 Predict Renal Function Decline in HumanSubjects with Type 2 Diabetes

Subject Selection

The study group included 404 individuals with type 2 diabetes andnormoalbuminuria (NA), microalbuminuria (MA), and proteinuria (PT),attending the Joslin clinic. Study group characteristics, sortedaccording to Albumin Excretion Rate (AER), are shown in Table 6.

TABLE 6 Characteristics of the study group according to AER status. NAMA PT Characteristics (n = 217) (n = 127) (n = 60) Age (yr)  55 ± 10 56± 9 58 ± 10 DM Duration (yr) 12 ± 8 14 ± 8 16 ± 8  cC-GFR (ml/min/1.73m2) 110 ± 30  95 ± 34 76 ± 32 cC-GFR categories >120 ml/min 32.3% 22.1%13.3% >90 ml/min 44.2% 29.9% 15.0% 60 to 89 ml/min 19.8% 32.3% 36.7% <60ml/min  3.7% 15.8% 35.0%

Serum Marker Analysis

Serum concentrations of TNFα, soluble TNF receptor 1 (sTNFR1), solubleTNF receptor 2 (sTNFR2), soluble intercellular and vascular adhesionmolecules (sICAM-1 and sVCAM-1, respectively), soluble Fas (sFas), IL-6,and CRP were measured in each of the subjects by ELISA or using theLuminex® platform. Results are shown below.

TABLE 7 Median plasma concentrations of biomarkers of inflammation,apoptosis, and endothelial function according to AER status andgroup-specific median GFR. NA MA PT Group Contrast Plasma GFR >108 GFR≦108 GFR >91 GFR ≦91 GFR >71 GFR ≦71 AER GFR markers (n = 111) (n = 106)(n = 64) (n = 63) (n = 30) (n = 30) P P TNFα 3.3 5.1 3.5 6.4 5.2 5.3<0.05 <0.0001 (pg/mL) sTNFR1 1009 1394 1162 1891 1516 2707 <0.0001<0.0001 (pg/mL) sTNFR2 1913 2614 2236 3416 2959 4801 <0.0001 <0.0001(pg/mL) sFas 5.0 6.8 5.3 7.7 7.2 9.3 <0.0001 <0.0001 (pg/mL) OPG 279 377316 425 359 476 <0.0001 NS (pg/mL) OPN 825 1080 1013 1716 1835 2918<0.005 NS (pg/mL) IL6 (pg/mL) 1.1 2.0 2.0 2.4 2.2 2.4 <0.0001 <0.001 CRP (mg/L) 1.8 4.0 4.1 3.9 3.7 5.0 <0.0005 NS sICAM-1 161 179 172 181191 176 <0.01 NS (ng/mL) sVCAM-1 450 481 404 481 481 523 <0.05 NS(ng/mL)

TABLE 8 Spearman correlation coefficients among plasma biomarkers ofinflammation, apoptosis, and endothelial function. TNFα sTNFR1 sTNFR2IL6 CRP sFas OPG OPN sICAM-1 sVCAM-1 GFR −0.48* −0.84* −0.79* −0.42*−0.23* −0.58* −0.44* −0.27* −0.21* −0.27* TNFα 1.00 0.49* 0.54* 0.28*0.15‡ 0.47* 0.38* 0.17† 0.31* 0.34* sTNFR1 1.00 0.89* 0.51* 0.29* 0.68*0.46* 0.24* 0.31* 0.33* sTNFR2 1.00 0.47* 0.27* 0.65* 0.41* 0.19† 0.34*0.38* IL6 1.00 0.58* 0.34* 0.33* 0.05 0.33* 0.13§ CRP 1.00 0.12§ 0.26*−0.05 0.25* −0.11§ sFas 1.00 0.53* 0.24* 0.33* 0.36* OPG 1.00 0.15‡0.29* 0.28* OPN 1.00 0.05 0.27* sICAM-1 1.00 0.22* sVCAM-1 1.00

A cross sectional analysis of the data presented in Tables 7-8 wasperformed. Specifically, marker levels were analyzed in 364 individualsfrom the group shown in Table 6 with GFR greater than or equal to 60mL/minute/1.73 m² and either normal urinary albumin excretion (NA; n=217of 346) or microalbuminuria (MA; n=129 of 346). The data is shown inTable 9. None of these subjects tested exhibited signs of ischemic heartdisease.

TABLE 9 Median Plasma Concentrations of Markers of Inflammation orApoptosis according to AER and GFR. Normoalbumuria Microalbuminuria cC-cC- cC- cC- GFR >118 GFR <118 GFR >107 GFR <107 Plasma marker n = 106 n= 105 n = 58 n = 55 TNFα (pg/mL) 3.3 4.7 3.4 5.3 sTNFR1 (ng/mL) 1.0 1.31.2 1.8 sTNFR2 (ng/mL) 1.9 2.6 2.2 3.1 sFas (pg/mL) 5.0 6.5 5.2 7.6 IL-6(pg/mL) 1.1 1.9 2.0 2.2 P = 0.0001

As shown above, higher concentrations of TNFα, sTNFR1, sTNFR2, sFas, andIL-6 were strongly associated with lower GFR in NA subjects and MAsubjects. These associations remained highly significant (p<0.0001)after adjustment for age, gender, and albuminuria status. Theassociations between GFR and CRP, sICAM-1, and sVCAM-1 were borderlinesignificant.

These observations suggest that serum evaluation of the markers TNFα,sTNFR1, sTNFR2, sFas, and IL-6 can be used to predict ERFD in type 2diabetics with NA and MA.

Example 4: Serum Concentrations of Soluble TNF Receptor 1 Predicts EndStage Renal Disease in Subjects with Baseline Proteinuria

Subject Selection

434 subjects attending Joslin Diabetes Clinic with type 1 diabetes andbaseline proteinuria were followed for an average of 8 years andprogression to end-stage renal disease (ESRD) as an outcome has beenevaluated.

Serum Marker Analysis

Serum concentrations of soluble TNF receptor 1 (sTNFR1) were measuredusing ELISA. The associations between the serum levels of sTNFR1 and endstage renal disease controlled for the baseline stage of chronic kidneydisease was then assessed. Results are shown in Table 10.

TABLE 10 sTNFR1 Levels in Subjects with Baseline Proteinuria TNFR1[pg/ml] ESRD Subject no. Median Q1 Q3 CKD <3 0 213 2078 1728 2437 1 282242 1756 3062 CKD = 3 0 78 3199 2719 3899 1 46 4156 3172 4772 CKD >3 09 5518 4869 8327 1 60 6605 5402 7590

As shown in Table 10, serum levels of sTNFR1 are associated with CKD (asassessed by GFR) and ESRD. The association between sTNFR1 and ESRDremained significant after adjustment for baseline stage CKD.

These observations support that sTNFR1 can be used to predict theprogression of diabetic nephropathy. Furthermore, these observationssupport that sTNR1 can be used to predict ESRD in patients with type 1diabetes and proteinuria.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

What is claimed is:
 1. A method of determining whether a human subjecthas an increased risk of developing early renal function decline (ERFD),the method comprising: obtaining a sample from a human subject who hasnormoalbuminuria (NA), microalbuminuria (MA), or proteinuria (PT);measuring levels of one or more biomarkers selected from the groupconsisting of Tumor Necrosis Factor alpha (TNFa), soluble TNF receptortype 1 (sTNFR1), soluble TNFR2 (sTNFR2), soluble Fas (sFas), andinterleukin-6 (IL-6), in the subject sample; comparing the subjectlevels with reference levels of said one or more biomarkers; anddetermining whether the subject has an increased risk of developing ERFDbased on the comparison of the subject levels with the reference levels.2. The method of claim 1, wherein the presence of levels of the one ormore biomarkers in the subject sample at levels that are significantlyhigher than the reference levels indicates that the subject has anincreased risk of developing ERFD.
 3. The method of claim 1, wherein thesample comprises serum from the subject.
 4. The method of claim 1,wherein the subject has diabetes.
 5. The method of claim 1, wherein thesubject has normoalbuminuria.
 6. The method of claim 1, wherein thesubject has microalbuminuria.
 7. The method of claim 1, wherein thesubject has proteinuria.
 8. The method of claim 1, wherein the subjecthas Type 1 diabetes.
 9. The method of claim 8, wherein the one or morebiomarkers comprise sTNFR1, sTNFR2, and sFas.
 10. The method of claim 9,wherein the one or more biomarkers comprise TNFa, sTNFR1, sTNFR2, andsFas.
 11. The method of claim 9, wherein the one or more biomarkerscomprise TNFa, sTNFR1, sTNFR2, sFas, and IL-6.
 12. The method of claim1, wherein the subject has Type 2 diabetes.
 13. The method of claim 12,wherein the one or more biomarkers comprise TNFa, sTNFR1, sTNFR2, sFas,and IL-6.
 14. The method of claim 1, wherein the subject does notpresent any clinical signs or symptoms of chronic heart disease (CHD).15. The method of claim 1, wherein the subject does not present anyclinical signs or symptoms of ischemic heart disease.
 16. The method ofclaim 1, wherein the subject has a glomerular filtration rate (GFR) of90 mL/minute or more.
 17. A method of determining whether a humansubject has an increased risk of developing chronic kidney disease(CKD), or end stage renal disease (ESRD), or both, the methodcomprising: obtaining a sample from a human subject who has proteinuria;measuring the level of soluble TNF receptor type 1 (STNFR1) in thesubject sample; comparing the subject levels of sTNFR1 with referencelevels of sTNFR1; and determining whether the subject has an increasedrisk of developing CKD or ESRD, or both, based on the comparison of thesubject levels with the reference levels.
 18. The method of claim 17,wherein the presence of sTNFR1 in the subject sample at levels that aresignificantly higher than the reference levels indicates that thesubject has an increased risk of developing CKD, ESRD, or both.
 19. Themethod of claim 17, wherein the subject has Type 1 or Type 2 diabetes.